Improved linear soft-input soft-output detection via soft feedback successive interference cancellation

Recently, iterative receiver combining multiple-input multiple-output (MIMO) detection with channel decoding has been widely considered to achieve near-capacity performance and reliable high data rate transmission, for future wireless communication systems. However, such iterative processing increases the computational complexity at the receiver. In this paper, the computational complexity of MIMO detection algorithms combined with turbo decoding is investigated. We first present an overview of the family of MIMO detection algorithms based on sphere decoding, K-Best decoding, and interference cancellation. A recently proposed low-complexity K-Best decoder (LC-K-Best) is also presented. Moreover, we analyze the convergence of combining these detection algorithms with the turbo decoder using the extrinsic information transfer (EXIT) chart. Consequently, a new scheduling order of the number of iterations for the iterative process is proposed. Several system configurations are developed and compared in terms of performance and complexity. Simulations and analytical results show that the new scheduling provides good performance with a large saving in the complexity. Additionally, the LC-K-Best decoder shows a good performance-complexity tradeoff, and it is therefore suitable for parallel and pipeline architectures that can meet high throughput requirements.

Section: Successive Interference Cancellation (Sic) Equalizermentioning

confidence: 99%

“…An improved VBLAST (I-VBLAST) for SISO detection was proposed in [18,19]. In addition, a SISO detector based on MMSE interference cancellation (MMSE-IC) was proposed in [20,21].…”

Section: Introductionmentioning

confidence: 99%

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Chall

Nouvel

Hélard

et al. 2015

“…Also, several pre-equalized methods have been proposed in [6][7][8][9][10] to reduce the time variations and obtain a quasi-diagonal channel matrix as convectional OFDM systems over the *Correspondence: huangyao@cuit.edu.cn Chengdu University of Information Technology, Sichuan, China slowly time-varying channels. Some decision feedback equalization techniques are also compatible with OFDM systems [11][12][13][14][15][16]. According to the previous literature, we can observe that the equalizers with the banded channel matrix can be efficiently performed in sequential or block manners.…”

Section: Introductionmentioning

confidence: 99%

Multi-segmental OFDM signals equalization with piecewise linear channel model over rapidly time-varying channels

Hua

Yao

et al. 2017

For rapidly time-varying channels, the performance of (orthogonal frequency division multiplexing) OFDM systems with the conventional one tap equalizer will be significantly degraded. Because the orthogonality between subcarriers is destroyed, the conventional way to combat the inter-carrier interference (ICI) is employing the banded minimum mean square error (MMSE) equalizer, which can save computational efforts introduced by a large number of subcarriers. However, the width of the banded channel matrix is mainly determined by the normalized Doppler frequency in the sense that with the high Doppler frequency the complexity of equalization for one OFDM block will significantly increase with the band width D. In order to reduce the equalization complexity, the authors proposed multi-segmental OFDM signal equalization method with piecewise linear model (PLM) to approximate the time variations and mitigate the corresponding ICI. Its complexity is significantly reduced with the small segments. Furthermore, an alternative MMSE method with the iterative rank-1 matrix updates is proposed to further reduce the complexity. We also derive the theoretical pre-equalized and equalized signal to interference ratio (SIR) for different normalized Doppler frequencies and segment numbers, which implies that the larger segment number can achieve the better performance. Simulation results demonstrate that the proposed method outperforms the conventional banded MMSE equalizer and the partial fast Fourier transform (FFT) method in terms of bit error rate (BER) with almost the same complexity.

“…By allowing subspaces to overlap, additional diversity can be gathered by putting a low reliable data stream into several detection sets. Other subspace methods employ projection operators and lists to generate candidates for interference cancellation in equalization schemes (e.g., [40][41][42][43][44]). …”

Section: Introductionmentioning

confidence: 99%

A low-complexity MIMO subspace detection algorithm

Mansour

2015

A low-complexity multiple-input multiple-output (MIMO) subspace detection algorithm is proposed. It is based on decomposing a MIMO channel into multiple subsets of decoupled streams that can be detected separately. The new scheme employs triangular decomposition followed by elementary matrix operations to transform the channel into a generalized elementary matrix whose structure matches the subsets of streams to be detected. The proposed approach avoids matrix inversion and allows subsets to overlap, thus achieving better diversity gain. An optimized detector architecture based on a 2-by-2 ML detector core is also presented. Simulations demonstrate that the proposed algorithm performs to within a few tenths of a dB from the optimum detection algorithm.